Tootfinder

Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Jonas Schmidinger, Viacheslav Barkov, Sebastian Vogel, Martin Atzmueller, Gerard B M Heuvelink
https://arxiv.org/abs/2509.09408

Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Machine learning and geostatistics are two fundamentally different frameworks for predicting and spatially mapping soil properties. Geostatistics leverages the spatial structure of soil properties, while machine learning captures the relationship between available environmental features and soil properties. We propose a hybrid framework that enriches ML with spatial context through engineering of 'spatial lag' features from ordinary kriging. We call this approach 'kriging prior regression' (KpR…

Protected Grounds and the System of Non-Discrimination Law in the Context of Algorithmic Decision-Making and Artificial Intelligence
Janneke Gerards, Frederik Zuiderveen Borgesius
https://arxiv.org/abs/2509.08837

Protected Grounds and the System of Non-Discrimination Law in the Context of Algorithmic Decision-Making and Artificial Intelligence
Algorithmic decision-making and similar types of artificial intelligence (AI) may lead to improvements in all sectors of society, but can also have discriminatory effects. While current non-discrimination law offers people some protection, algorithmic decision-making presents the law with several challenges. For instance, algorithms can generate new categories of people based on seemingly innocuous characteristics, such as web browser preference or apartment number, or more complicated categori…

Cross-Platform Narrative Prediction: Leveraging Platform-Invariant Discourse Networks
Patrick Gerard, Luca Luceria, Leonardo Blas, Emilio Ferrara
https://arxiv.org/abs/2510.09464

Cross-Platform Narrative Prediction: Leveraging Platform-Invariant Discourse Networks
Online narratives spread unevenly across platforms, with content emerging on one site often appearing on others, hours, days or weeks later. Existing cross-platform information diffusion models often treat platforms as isolated systems, disregarding cross-platform activity that might make these patterns more predictable. In this work, we frame cross-platform prediction as a network proximity problem: rather than tracking individual users across platforms or relying on brittle signals like share…

Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
Kerem Delikoyun, Qianyu Chen, Liu Wei, Si Ko Myo, Johannes Krell, Martin Schlegel, Win Sen Kuan, John Tshon Yit Soong, Gerhard Schneider, Clarissa Prazeres da Costa, Percy A. Knolle, Laurent Renia, Matthew Edward Cove, Hwee Kuan Lee, Klaus Diepold, Oliver Hayden

Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
While analysing rare blood cell aggregates remains challenging in automated haematology, they could markedly advance label-free functional diagnostics. Conventional flow cytometers efficiently perform cell counting with leukocyte differentials but fail to identify aggregates with flagged results, requiring manual reviews. Quantitative phase imaging flow cytometry captures detailed aggregate morphologies, but clinical use is hampered by massive data storage and offline processing. Incorporating …

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