Zobrazeno 1 - 10
of 5 948
pro vyhledávání: '"Maass, P."'
Publikováno v:
NeurIPS 2024 Workshop on Time Series in the Age of Large Models
Anomaly detection in clinical time-series holds significant potential in identifying suspicious patterns in different biological parameters. In this paper, we propose a targeted method that incorporates the clinical domain knowledge into LLMs to impr
Externí odkaz:
http://arxiv.org/abs/2410.12830
Autor:
Wagner, Martin W., Domburg, Freek, Krüger, Carsten, Meyer, Jens, Zhang, Jiaqi, Ramesh, Prashanth, Maass, Corinna C.
Magnetotaxis is a well known phenomenon in swimming microorganisms which sense magnetic fields e.g. by incorporating crystalline magnetosomes. In designing artificial active matter with tunable dynamics, external magnetic fields can provide a versati
Externí odkaz:
http://arxiv.org/abs/2409.14558
Autor:
Kar, Satyakam, Ikeda, Yuki, Nielsch, Kornelius, Reith, Heiko, Maaß, Robert, Fähler, Sebastian
Ferroic materials enable a multitude of emerging applications, and optimum functional properties are achieved when ferromagnetic and ferroelectric properties are coupled to a first-order ferroelastic transition. In bulk materials, this first-order tr
Externí odkaz:
http://arxiv.org/abs/2408.00364
Publikováno v:
Boundary-Layer Meteorol. 190, 48 (2024)
We determine distributions and correlation properties of offshore wind speeds and wind speed increments by analyzing wind data sampled with a resolution of one second for 20 months at different heights above sea level in the North Sea. Distributions
Externí odkaz:
http://arxiv.org/abs/2407.12934
Autor:
Passon, Stephan, König, Kristian, Schilling, Florian, Maaß, Bernhard, Meisner, Johann, Nörtershäuser, Wilfried
This paper presents the concept of an ultra-stable, thermally independent precision voltage divider tailored for direct current (DC) voltages up to 60 kV. Key features of this voltage divider include minimal voltage dependence, excellent stability, a
Externí odkaz:
http://arxiv.org/abs/2407.06700
Autor:
Herdt, Rudolf, Kinzel, Louisa, Maaß, Johann Georg, Walther, Marvin, Fröhlich, Henning, Schubert, Tim, Maass, Peter, Schaaf, Christian Patrick
Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactions, and developmental stages of animals, various deep learning approac
Externí odkaz:
http://arxiv.org/abs/2405.12957
We present an efficient method to perform overdamped Brownian dynamics simulations in external force fields and for particle interactions that include a hardcore part. The method applies to particle motion in one dimension, where it is possible to up
Externí odkaz:
http://arxiv.org/abs/2406.04972
Autor:
Maass, Javier, Fontbona, Joaquin
We develop a Mean-Field (MF) view of the learning dynamics of overparametrized Artificial Neural Networks (NN) under data symmetric in law wrt the action of a general compact group $G$. We consider for this a class of generalized shallow NNs given by
Externí odkaz:
http://arxiv.org/abs/2405.19995
Autor:
Herdt, Rudolf, Maass, Peter
We investigate how generated structures of GANs correlate with their activations in hidden layers, with the purpose of better understanding the inner workings of those models and being able to paint structures with unconditionally trained GANs. This
Externí odkaz:
http://arxiv.org/abs/2405.15636
Autor:
Denker, Alexander, Kereta, Zeljko, Singh, Imraj, Freudenberg, Tom, Kluth, Tobias, Maass, Peter, Arridge, Simon
Electrical impedance tomography (EIT) plays a crucial role in non-invasive imaging, with both medical and industrial applications. In this paper, we present three data-driven reconstruction methods for EIT imaging. These three approaches were origina
Externí odkaz:
http://arxiv.org/abs/2407.01559