Titlebook: New Trends in Database and Information Systems; ADBIS 2024 Short Pap Joe Tekli,Johann Gamper,Ester Zumpano Conference proceedings 2025 The

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Gerard Pons,Miona Dimic,Besim Bilalli with a clear and deep understanding of TEM, the essential tThis text is a companion volume to .Transmission Electron Microscopy: A Textbook for Materials Science. by Williams and Carter. The aim is to extend the discussion of certain topics that are either rapidly changing at this time or that woul

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Márcia Jacobina Andrade Martins,Claudia Bauzer Medeirosng the processing-structure-prop­ croscopist in order to do the job properly: crystallography, erties links down to atomistic levels. It now is even possible diffraction, image contrast, inelastic scattering events, and to tailor the microstructure (and meso structure ) of materials spectroscopy. Re

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Tiago Toledo Junior,Diego Raphael Amancio,Roseli Aparecida F. Romerong the processing-structure-prop­ croscopist in order to do the job properly: crystallography, erties links down to atomistic levels. It now is even possible diffraction, image contrast, inelastic scattering events, and to tailor the microstructure (and meso structure ) of materials spectroscopy. Re

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Mirian Halfeld-Ferrari,Anne-Lyse Minard,Genoveva Vargas-Solarng the processing-structure-prop­ croscopist in order to do the job properly: crystallography, erties links down to atomistic levels. It now is even possible diffraction, image contrast, inelastic scattering events, and to tailor the microstructure (and meso structure ) of materials spectroscopy. Re

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Entity Matching with Large Language Models as Weak and Strong Labellersrong LLM provides labels with better precision, but greatly increased cost. To address this, we develop a technique capable of intelligently allocating labels between strong and weak LLM labellers to maximise performance within the given budget. We show that, given a small amount of already labelled

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1865-0929 Personalization and Recommender Systems (PERS 2024); Access methods and query processing; discovery and data analysis; Machine Learning; large language models; and tutorials. .978-3-031-70420-8978-3-031-70421-5Series ISSN 1865-0929 Series E-ISSN 1865-0937