Zobrazeno 1 - 10
of 6 463
pro vyhledávání: '"Hannemann A"'
Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights into patient health. To predict diseases or provide individualized the
Externí odkaz:
http://arxiv.org/abs/2410.15840
Genome-wide association studies are pivotal in understanding the genetic underpinnings of complex traits and diseases. Collaborative, multi-site GWAS aim to enhance statistical power but face obstacles due to the sensitive nature of genomic data shar
Externí odkaz:
http://arxiv.org/abs/2410.08122
Autor:
Lamp, Josephine, Derdzinski, Mark, Hannemann, Christopher, Hatfield, Sam, van der Linden, Joost
Many time series, particularly health data streams, can be best understood as a sequence of phenomenon or events, which we call motifs. A time series motif is a short trace segment which may implicitly capture an underlying phenomenon within the time
Externí odkaz:
http://arxiv.org/abs/2409.15219
Interactions of polyelectrolytes (PEs) with proteins play a crucial role in numerous biological processes, such as the internalization of virus particles into host cells. Although docking, machine learning methods, and molecular dynamics (MD) simulat
Externí odkaz:
http://arxiv.org/abs/2409.00210
Traffic assignment is a core component of many urban transport planning tools. It is used to determine how traffic is distributed over a transportation network. We study the task of computing traffic assignments for public transport: Given a public t
Externí odkaz:
http://arxiv.org/abs/2408.06308
Autor:
Kelm, André, Hannemann, Niels, Heberle, Bruno, Schmidt, Lucas, Rolff, Tim, Wilms, Christian, Yaghoubi, Ehsan, Frintrop, Simone
This study introduces a novel expert generation method that dynamically reduces task and computational complexity without compromising predictive performance. It is based on a new hierarchical classification network topology that combines sequential
Externí odkaz:
http://arxiv.org/abs/2403.05601
Machine learning on large-scale genomic or transcriptomic data is important for many novel health applications. For example, precision medicine tailors medical treatments to patients on the basis of individual biomarkers, cellular and molecular state
Externí odkaz:
http://arxiv.org/abs/2402.14527
Autor:
Bruers, Ben, Cruces, Marilyn, Demleitner, Markus, Duckeck, Guenter, Düren, Michael, Eich, Niclas, Enßlin, Torsten, Erdmann, Johannes, Erdmann, Martin, Fackeldey, Peter, Felder, Christian, Fischer, Benjamin, Fröse, Stefan, Funk, Stefan, Gasthuber, Martin, Grimshaw, Andrew, Hadasch, Daniela, Hannemann, Moritz, Kappes, Alexander, Kleinemühl, Raphael, Kozlov, Oleksiy M., Kuhr, Thomas, Lupberger, Michael, Neuhaus, Simon, Niknejadi, Pardis, Reindl, Judith, Schindler, Daniel, Schneidewind, Astrid, Schreiber, Frank, Schumacher, Markus, Schwarz, Kilian, Streit, Achim, von Cube, R. Florian, Walker, Rod, Walther, Cyrus, Wozniewski, Sebastian, Zhou, Kai
Given the urgency to reduce fossil fuel energy production to make climate tipping points less likely, we call for resource-aware knowledge gain in the research areas on Universe and Matter with emphasis on the digital transformation. A portfolio of m
Externí odkaz:
http://arxiv.org/abs/2311.01169
Autor:
Lei, Zhihong, Pusateri, Ernest, Han, Shiyi, Liu, Leo, Xu, Mingbin, Ng, Tim, Travadi, Ruchir, Zhang, Youyuan, Hannemann, Mirko, Siu, Man-Hung, Huang, Zhen
Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as contact names remains a challenge. In this work, we describe our person
Externí odkaz:
http://arxiv.org/abs/2310.09988
Autor:
Lei, Zhihong, Xu, Mingbin, Han, Shiyi, Liu, Leo, Huang, Zhen, Ng, Tim, Zhang, Yuanyuan, Pusateri, Ernest, Hannemann, Mirko, Deng, Yaqiao, Siu, Man-Hung
Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level. The E2E systems implicitly model all conventional ASR components, such as the acoustic model
Externí odkaz:
http://arxiv.org/abs/2310.07062