Zobrazeno 1 - 10
of 79 571
pro vyhledávání: '"Bertram, A. A."'
Autor:
Benestad, Jacob, Rasmussen, Torbjørn, Brovang, Bertram, Krause, Oswin, Fallahi, Saeed, Gardner, Geoffrey C., Manfra, Michael J., Marcus, Charles M., Danon, Jeroen, Kuemmeth, Ferdinand, Chatterjee, Anasua, van Nieuwenburg, Evert
We investigate automated in situ optimization of the potential landscape in a quantum point contact device, using a $3 \times 3$ gate array patterned atop the constriction. Optimization is performed using the covariance matrix adaptation evolutionary
Externí odkaz:
http://arxiv.org/abs/2412.04997
Autor:
Düring, Bertram, Wright, Oliver
Voter demographics and socio-economic factors like age, sex, ethnicity, education level, income, and other measurable factors like behaviour in previous elections or referenda are of key importance in modelling opinion formation dynamics. Here, we re
Externí odkaz:
http://arxiv.org/abs/2412.02461
Autor:
Feldt, Markus, Bertram, Thomas, Correia, Carlos, Absil, Olivier, Vázquez, M. Concepción Cárdenas, Coppejans, Hugo, Kulas, Martin, Obereder, Andreas, de Xivry, Gilles Orban, Scheithauer, Silvia, Steuer, Horst
The Mid-infrared ELT Imager and Spectrograph (METIS) is a first-generation instrument for the Extremely Large Telescope (ELT), Europe's next-generation 39 m ground-based telescope for optical and infrared wavelengths. METIS will offer diffraction-lim
Externí odkaz:
http://arxiv.org/abs/2411.17341
Autor:
Bitsch, Bertram, Izidoro, Andre
Migration is a key ingredient for the formation of close-in super-Earth and mini-Neptune systems, as it sets in which resonances planets can be trapped. Slower migration rates result in wider resonance configurations compared to higher migration rate
Externí odkaz:
http://arxiv.org/abs/2411.11452
Autor:
Rosbach, Emely, Ammeling, Jonas, Krügel, Sebastian, Kießig, Angelika, Fritz, Alexis, Ganz, Jonathan, Puget, Chloé, Donovan, Taryn, Klang, Andrea, Köller, Maximilian C., Bolfa, Pompei, Tecilla, Marco, Denk, Daniela, Kiupel, Matti, Paraschou, Georgios, Kok, Mun Keong, Haake, Alexander F. H., de Krijger, Ronald R., Sonnen, Andreas F. -P., Kasantikul, Tanit, Dorrestein, Gerry M., Smedley, Rebecca C., Stathonikos, Nikolas, Uhl, Matthias, Bertram, Christof A., Riener, Andreas, Aubreville, Marc
Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, such as confirmation bia
Externí odkaz:
http://arxiv.org/abs/2411.01007
The advent of large language models (LLMs), such as GPT-4, has enabled significant advancements in generating code across various domains. However, these models face unique challenges when generating IEC 61131-3 Structured Text (ST) code due to limit
Externí odkaz:
http://arxiv.org/abs/2410.22159
Autor:
Bertram, Wolfgang
We investigate the problem of defining group or loop structures on spheres, where by ''sphere'' we mean the level set q(x) = c of a general K-valued quadratic form q, for an invertible scalar c. When K is a field and q non-degenerate, then this corre
Externí odkaz:
http://arxiv.org/abs/2410.17634
Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, prior work has mostly explored naive solutions such as fine-tuning, focus
Externí odkaz:
http://arxiv.org/abs/2410.09681
Publikováno v:
2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), Vienna, Austria, 2024, pp. 602-609
Provenance in databases has been thoroughly studied for positive and for recursive queries, then for first-order (FO) queries, i.e., having negation but no recursion. Query evaluation can be understood as a two-player game where the opponents argue w
Externí odkaz:
http://arxiv.org/abs/2410.05094
Prompt-based fine-tuning has become an essential method for eliciting information encoded in pre-trained language models for a variety of tasks, including text classification. For multi-class classification tasks, prompt-based fine-tuning under low-r
Externí odkaz:
http://arxiv.org/abs/2410.01946