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Accettura, Carlotta, Adrian, Simon, Agarwal, Rohit, Ahdida, Claudia, Aimé, Chiara, Aksoy, Avni, Alberghi, Gian Luigi, Alden, Siobhan, Alfonso, Luca, Amapane, Nicola, Amorim, David, Andreetto, Paolo, Anulli, Fabio, Appleby, Rob, Apresyan, Artur, Asadi, Pouya, Mahmoud, Mohammed Attia, Auchmann, Bernhard, Back, John, Badea, Anthony, Bae, Kyu Jung, Bahng, E. J., Balconi, Lorenzo, Balli, Fabrice, Bandiera, Laura, Barbagallo, Carmelo, Barlow, Roger, Bartoli, Camilla, Bartosik, Nazar, Barzi, Emanuela, Batsch, Fabian, Bauce, Matteo, Begel, Michael, Berg, J. 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This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given t
Externí odkaz:
http://arxiv.org/abs/2411.02966
Publikováno v:
Proceedings of the 11'th International Heavy Haul Association Conference (IHHA 2017), pages 799-805 Cape Town, South Africa
Rail breaks are one of the most common causes of derailments internationally. This is no different for the South African Iron Ore line. Many rail breaks occur as a heavy-haul train passes over a crack, large defect or defective weld. In such cases, i
Externí odkaz:
http://arxiv.org/abs/2208.11940
Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in
Externí odkaz:
http://arxiv.org/abs/2208.09299
Tidigare forskning pekar på att det kan finnas samband mellan vissa psykologiska faktorer och utveckling av depression och ångest. Därför var syftet med denna studie att undersöka huruvida olika psykologiska faktorer påverkar risken att u
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-183382
Latent Dirichlet Allocation (LDA) is a probabilistic model used to uncover latent topics in a corpus of documents. Inference is often performed using variational Bayes (VB) algorithms, which calculate a lower bound to the posterior distribution over
Externí odkaz:
http://arxiv.org/abs/2111.01480
Publikováno v:
In Science and Information Conference (pp. 723-746). Springer, Cham (2022)
Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in
Externí odkaz:
http://arxiv.org/abs/2110.00635