Technische Hochschule Würzburg-Schweinfurt Publikationsserver OPUS
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„Nur Etikettenschwindel? Unterfränkische Energieversorger bieten Öko-Erdgas mit zweifelhaften Zertifikaten an“, im Gespräch mit der MainPost zu Klimaschutz-Angeboten von Erdgas-Anbietern [Interview]
No full textThe Order of the International Court of Justice on the Gaza War: Significance and Limitations
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Sichere Mensch-Roboter-Kollaboration in der Montage – Innovative Sicherheitskonzepte für eine effiziente Zusammenarbeit
No full textRezension zu "Kinder psychisch erkrankter Eltern" von Sabine Wagenblass & Christian Spatscheck
No full textMathematical Analysis and Numerical Methods: IACMC 2023, Zarqa, Jordan, May 10–12
No full textThis book presents a thoughtful compilation of chapters derived from the proceedings of the 8th International Arab Conference on Mathematics and Computations (IACMC 2023), held at Zarqa University in Zarqa, Jordan, from 10–12 May 2023. Encompassing a broad spectrum of themes crucial to contemporary research and development, the book delved into subjects ranging from partial and differential equations to fractional calculus, from probability and statistics to graph theory, and from approximation theory to nonlinear dynamics. Moreover, it explores pivotal areas such as numerical analysis and methods, as well as fostering interdisciplinary mathematical research initiatives.
Building upon the legacy of its predecessors, IACMC 2023 served as a premier platform for scholars, researchers and industry professionals to converge and exchange insights on a myriad of cutting-edge advancements and practical applications within the realm of mathematical sciences. This volume encapsulates the essence of IACMC 2023, offering readers a comprehensive overview of the latest breakthroughs and trends in mathematical sciences while serving as a testament to the collaborative spirit and intellectual vigor that define this esteemed conference series
Neue Qualitätskriterien für die Patientenschulung – Ergebnisse eines mehrstufigen Delphi-Prozesses
No full textRefinement of Sparsely Tagged Ground Truth Paths Using PDR and Particle Filter Smoothing
No full textBPE Gets Picky: Efficient Vocabulary Refinement During Tokenizer Training
No full textLanguage models can greatly benefit from efficient tokenization. However, they still mostly utilize the classical Byte-Pair Encoding (BPE) algorithm, a simple and reliable method. BPE has been shown to cause such issues as under-trained tokens and sub-optimal compression that may affect the downstream performance. We introduce PickyBPE, a modified BPE algorithm that carries out vocabulary refinement during tokenizer training by removing merges that leave intermediate “junk” tokens. Our method improves vocabulary efficiency, eliminates under-trained tokens, and does not compromise text compression. Our experiments show that this method either improves downstream performance or does not harm it