Zobrazeno 1 - 10
of 16 653
pro vyhledávání: '"P Willems"'
Autor:
Willems, Robin, Förster, Peter, Schöps, Sebastian, van der Sluis, Olaf, Verhoosel, Clemens V.
Cardio-mechanical models can be used to support clinical decision-making. Unfortunately, the substantial computational effort involved in many cardiac models hinders their application in the clinic, despite the fact that they may provide valuable inf
Externí odkaz:
http://arxiv.org/abs/2411.08822
Uncertainty in the aging of batteries in battery electric vehicles impacts both the daily driving range as well as the expected economic lifetime. This paper presents a method to determine online the capacity and internal resistance of a battery cell
Externí odkaz:
http://arxiv.org/abs/2410.03528
Drug discovery is a complex and resource-intensive process, with significant time and cost investments required to bring new medicines to patients. Recent advancements in generative machine learning (ML) methods offer promising avenues to accelerate
Externí odkaz:
http://arxiv.org/abs/2410.02718
This study aims to investigate whether GPT-4 can effectively grade assignments for design university students and provide useful feedback. In design education, assignments do not have a single correct answer and often involve solving an open-ended de
Externí odkaz:
http://arxiv.org/abs/2409.17698
Autor:
Strafforello, Ombretta, Soydaner, Derya, Willems, Michiel, Maerten, Anne-Sofie, De Winter, Stefanie
The emergence of large Vision-Language Models (VLMs) has recently established new baselines in image classification across multiple domains. However, the performance of VLMs in the specific task of artwork classification, particularly art style class
Externí odkaz:
http://arxiv.org/abs/2409.03521
Autor:
Mariappan, Panchatchram, Willems, Klaas, Boregowda, Gangadhara, Tiwari, Sudarshan, Klar, Axel
In this paper, we present high-performance computing for the BGK model of the Boltzmann equations with a meshfree method. We use the Arbitrary-Lagrangian-Eulerian (ALE) method, where the approximation of spatial derivatives and the reconstruction of
Externí odkaz:
http://arxiv.org/abs/2408.02350
We propose a straightforward yet highly effective few-shot fine-tuning strategy for adapting the Segment Anything (SAM) to anatomical segmentation tasks in medical images. Our novel approach revolves around reformulating the mask decoder within SAM,
Externí odkaz:
http://arxiv.org/abs/2407.04651
Deep convolutional neural networks are widely used in medical image segmentation but require many labeled images for training. Annotating three-dimensional medical images is a time-consuming and costly process. To overcome this limitation, we propose
Externí odkaz:
http://arxiv.org/abs/2407.04638
Autor:
Willems, Joshua
Let the abstract fractional space--time operator $(\partial_t + A)^s$ be given, where $s \in (0,\infty)$ and $-A \colon \mathsf{D}(A) \subseteq X \to X$ is a linear operator generating a uniformly bounded strongly measurable semigroup $(S(t))_{t\ge0}
Externí odkaz:
http://arxiv.org/abs/2404.11289
In this paper, we consider the problem of estimating the causal effect of an endogenous variable $Z$ on a survival time $T$ that can be subject to different types of dependent censoring. Firstly, we extend the current literature by simultaneously all
Externí odkaz:
http://arxiv.org/abs/2403.11860