Zobrazeno 1 - 10
of 26 065
pro vyhledávání: '"Schoen, A."'
Autor:
Shi, Jiaojian, Heide, Christian, Xu, Haowei, Huang, Yijing, Shen, Yuejun, Guzelturk, Burak, Henstridge, Meredith, Schön, Carl Friedrich, Mangu, Anudeep, Kobayashi, Yuki, Peng, Xinyue, Zhang, Shangjie, May, Andrew F., Reddy, Pooja Donthi, Shautsova, Viktoryia, Taghinejad, Mohammad, Luo, Duan, Hughes, Eamonn, Brongersma, Mark L., Mukherjee, Kunal, Trigo, Mariano, Heinz, Tony F., Li, Ju, Nelson, Keith A., Baldini, Edoardo, Zhou, Jian, Ghimire, Shambhu, Wuttig, Matthias, Reis, David A., Lindenberg, Aaron M.
Important advances have recently been made in the search for materials with complex multi-phase landscapes that host photoinduced metastable collective states with exotic functionalities. In almost all cases so far, the desired phases are accessed by
Externí odkaz:
http://arxiv.org/abs/2411.10131
Autor:
Krauß, Veronika, McGill, Mark, Kosch, Thomas, Thiel, Yolanda, Schön, Dominik, Gugenheimer, Jan
With the recent advancements in Large Language Models (LLMs), web developers increasingly apply their code-generation capabilities to website design. However, since these models are trained on existing designerly knowledge, they may inadvertently rep
Externí odkaz:
http://arxiv.org/abs/2411.03108
For the validation and verification of automotive radars, datasets of realistic traffic scenarios are required, which, how ever, are laborious to acquire. In this paper, we introduce radar scene synthesis using GANs as an alternative to the real data
Externí odkaz:
http://arxiv.org/abs/2410.13526
Adversarial training can be used to learn models that are robust against perturbations. For linear models, it can be formulated as a convex optimization problem. Compared to methods proposed in the context of deep learning, leveraging the optimizatio
Externí odkaz:
http://arxiv.org/abs/2410.12677
Image monitoring and guidance during medical examinations can aid both diagnosis and treatment. However, the sampling frequency is often too low, which creates a need to estimate the missing images. We present a probabilistic motion model for sequent
Externí odkaz:
http://arxiv.org/abs/2410.11491
Since neural networks can make wrong predictions even with high confidence, monitoring their behavior at runtime is important, especially in safety-critical domains like autonomous driving. In this paper, we combine ideas from previous monitoring app
Externí odkaz:
http://arxiv.org/abs/2410.06051
Capturing images using multispectral camera arrays has gained importance in medical, agricultural and environmental processes. However, using all available spectral bands is infeasible and produces much data, while only a fraction is needed for a giv
Externí odkaz:
http://arxiv.org/abs/2410.02001
For run sizes that are a multiple of four, the literature offers many two-level designs that are D- and A-optimal for the main-effects model and minimize the aliasing between main effects and interaction effects and among interaction effects. For run
Externí odkaz:
http://arxiv.org/abs/2409.13336
Diffusion models have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences. Recently, these models have also been applied to low-level computer vision for photo-realistic image
Externí odkaz:
http://arxiv.org/abs/2409.10353
Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those found in image processing and language models. Despite their success in t
Externí odkaz:
http://arxiv.org/abs/2409.09650