Zobrazeno 1 - 10
of 7 171
pro vyhledávání: '"Frank Michael"'
Autor:
Frank Michael Schmidt-Döhl, David Schulenberg, Franziska Tralow, Jürgen Neubauer, Julian Johannes Wolf, Dominique Ectors
Publikováno v:
Acta Polytechnica CTU Proceedings, Vol 33, Pp 539-545 (2022)
The C-S-H-phase is the most important strength generating phase in concrete and other cementitious materials. The analysis of C-S-H is therefore an important instrument of innovations in the field of concrete and its durability and sustainability. Th
Externí odkaz:
https://doaj.org/article/ba6c5ff07b7841d585b8f833db0158c4
Expert systems are effective tools for automatically identifying energy efficiency potentials in manufacturing, thereby contributing significantly to global climate targets. These systems analyze energy data, pinpoint inefficiencies, and recommend op
Externí odkaz:
http://arxiv.org/abs/2411.01272
Autor:
Röder, Manuel, Schleif, Frank-Michael
This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients t
Externí odkaz:
http://arxiv.org/abs/2409.12575
Autor:
Julia Kopanz, Julia K. Mader, Klaus Donsa, Angela Libiseller, Felix Aberer, Marlene Pandis, Johanna Reinisch-Gratzer, Gisela C. Ambrosch, Bettina Lackner, Thomas Truskaller, Frank Michael Sinner, Thomas R. Pieber, Katharina M. Lichtenegger
Publikováno v:
Frontiers in Clinical Diabetes and Healthcare, Vol 3 (2022)
GlucoTab@MobileCare, a digital workflow and decision support system with integrated basal and basal-plus insulin algorithm was investigated for user acceptance, safety and efficacy in persons with type 2 diabetes receiving home health care by nurses.
Externí odkaz:
https://doaj.org/article/93dfffa725ca4979a68d54ad8996b760
Autor:
Röder, Manuel, Schleif, Frank-Michael
We present a numerically robust, computationally efficient approach for non-I.I.D. data stream sampling in federated client systems, where resources are limited and labeled data for local model adaptation is sparse and expensive. The proposed method
Externí odkaz:
http://arxiv.org/abs/2408.17108
While high-performing language models are typically trained on hundreds of billions of words, human children become fluent language users with a much smaller amount of data. What are the features of the data they receive, and how do these features su
Externí odkaz:
http://arxiv.org/abs/2408.03617
Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electr
Externí odkaz:
http://arxiv.org/abs/2407.04377
Autor:
Long, Bria, Xiang, Violet, Stojanov, Stefan, Sparks, Robert Z., Yin, Zi, Keene, Grace E., Tan, Alvin W. M., Feng, Steven Y., Zhuang, Chengxu, Marchman, Virginia A., Yamins, Daniel L. K., Frank, Michael C.
Human children far exceed modern machine learning algorithms in their sample efficiency, achieving high performance in key domains with much less data than current models. This ''data gap'' is a key challenge both for building intelligent artificial
Externí odkaz:
http://arxiv.org/abs/2406.10447
Autor:
Tan, Alvin Wei Ming, Yu, Sunny, Long, Bria, Ma, Wanjing Anya, Murray, Tonya, Silverman, Rebecca D., Yeatman, Jason D., Frank, Michael C.
How (dis)similar are the learning trajectories of vision-language models and children? Recent modeling work has attempted to understand the gap between models' and humans' data efficiency by constructing models trained on less data, especially multim
Externí odkaz:
http://arxiv.org/abs/2406.10215
Autor:
Frank Michael Lehmann, Nicole von Burg, Robert Ivanek, Claudia Teufel, Edit Horvath, Annick Peter, Gleb Turchinovich, Daniel Staehli, Tobias Eichlisberger, Mercedes Gomez de Agüero, Mairene Coto-Llerena, Michaela Prchal-Murphy, Veronika Sexl, Mohamed Bentires-Alj, Christoph Mueller, Daniela Finke
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-15 (2020)
Group 3 innate lymphoid cells (ILC3s) promote T cell activation in the spleen but suppress it in the gut. Here, the authors show that this distinct regulation is mediated by gut microbiota-induced IL-23 and IFN-γ, respectively, and, along with the a
Externí odkaz:
https://doaj.org/article/214732bb64544cc99449dba97da1e82b