Zobrazeno 1 - 10
of 84 608
pro vyhledávání: '"Beer BE"'
Autor:
Wolf, Daniel, Payer, Tristan, Lisson, Catharina Silvia, Lisson, Christoph Gerhard, Beer, Meinrad, Götz, Michael, Ropinski, Timo
Publikováno v:
Computers in Biology and Medicine, Volume 183, 2024
Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive pre-training on medical images. However, further research is nec
Externí odkaz:
http://arxiv.org/abs/2410.14524
Autor:
de Beer, Alex, Bjarkason, Elvar K, Gravatt, Michael, Nicholson, Ruanui, O'Sullivan, John P, O'Sullivan, Michael J, Maclaren, Oliver J
Numerical models of geothermal reservoirs typically depend on hundreds or thousands of unknown parameters, which must be estimated using sparse, noisy data. However, these models capture complex physical processes, which frequently results in long ru
Externí odkaz:
http://arxiv.org/abs/2410.09017
Autor:
Beer, Anna, Weber, Pascal, Miklautz, Lukas, Leiber, Collin, Durani, Walid, Böhm, Christian, Plant, Claudia
Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analysis with Density-based Exploration), the first deep clustering algorith
Externí odkaz:
http://arxiv.org/abs/2410.06265
Autor:
Wang, Weichen, Cantalupo, Sebastiano, Pensabene, Antonio, Galbiati, Marta, Travascio, Andrea, Steidel, Charles C., Maseda, Michael V., Pezzulli, Gabriele, de Beer, Stephanie, Fossati, Matteo, Fumagalli, Michele, Gallego, Sofia G., Lazeyras, Titouan, Mackenzie, Ruari, Matthee, Jorryt, Nanayakkara, Themiya, Quadri, Giada
Observational studies showed that galaxy disks are already in place in the first few billion years of the universe. The early disks detected so far, with typical half-light radii of 3 kiloparsecs at stellar masses around 10^11 M_sun for redshift z~3,
Externí odkaz:
http://arxiv.org/abs/2409.17956
Autor:
Beer, Robin, Feix, Alexander, Guttzeit, Tim, Muras, Tamara, Müller, Vincent, Rauscher, Maurice, Schäffler, Florian, Löwe, Welf
Large language models (LLMs), such as ChatGPT and Copilot, are transforming software development by automating code generation and, arguably, enable rapid prototyping, support education, and boost productivity. Therefore, correctness and quality of t
Externí odkaz:
http://arxiv.org/abs/2408.16601
In the era of big data, machine learning (ML) has become a powerful tool in various fields, notably impacting structural dynamics. ML algorithms offer advantages by modeling physical phenomena based on data, even in the absence of underlying mechanis
Externí odkaz:
http://arxiv.org/abs/2408.08629
The European Central Bank is preparing for the potential issuance of a central bank digital currency (CBDC), called the digital euro. A recent regulatory proposal by the European Commission defines several requirements for the digital euro, such as s
Externí odkaz:
http://arxiv.org/abs/2408.06956
We introduce MOSCITO (MOlecular Dynamics Subspace Clustering with Temporal Observance), a subspace clustering for molecular dynamics data. MOSCITO groups those timesteps of a molecular dynamics trajectory together into clusters in which the molecule
Externí odkaz:
http://arxiv.org/abs/2408.00056
Autor:
de Beer, Alex, Power, Andrew, Wong, Daniel, Dekkers, Ken, Gravatt, Michael, Bjarkason, Elvar K., O'Sullivan, John P., O'Sullivan, Michael J., Maclaren, Oliver J., Nicholson, Ruanui
The ability to make accurate predictions with quantified uncertainty provides a crucial foundation for the successful management of a geothermal reservoir. Conventional approaches for making predictions using geothermal reservoir models involve estim
Externí odkaz:
http://arxiv.org/abs/2407.15401
Publikováno v:
Reliability Engineering & System Safety (2024) 110309
Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node fai
Externí odkaz:
http://arxiv.org/abs/2407.11053