Zobrazeno 1 - 10
of 47 484
pro vyhledávání: '"A. Hamm"'
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-4-W2-2022, Pp 97-104 (2022)
Urban dynamics modelling using system dynamic (SD) approaches aims to provide an understanding of the major internal forces within an urban area, such as population development. SD models provide valuable information for decision and policy making. U
Externí odkaz:
https://doaj.org/article/8f16bb75743240968131891ad27a45cf
Autor:
A. Hamm, A. Frampton
Publikováno v:
The Cryosphere, Vol 15, Pp 4853-4871 (2021)
Modeling the physical state of permafrost landscapes is a crucial addition to field observations in order to understand the feedback mechanisms between permafrost and the atmosphere within a warming climate. A common hypothesis in permafrost modeling
Externí odkaz:
https://doaj.org/article/a427a4fbbd58408b83fcd131b7e34454
Current AI-assisted skin image diagnosis has achieved dermatologist-level performance in classifying skin cancer, driven by rapid advancements in deep learning architectures. However, unlike traditional vision tasks, skin images in general present un
Externí odkaz:
http://arxiv.org/abs/2409.09520
Parametrizing energy functions for ionic systems can be challenging. Here, the total energy function for an eutectic system consisting of water, SCN$^-$, K$^+$ and acetamide is improved vis-a-vis experimentally measured properties. Given the importan
Externí odkaz:
http://arxiv.org/abs/2408.07638
Through comprehensive data analysis, we demonstrate that a ${\chi}^{(2)}$-induced artifact, arising from imperfect balancing in the conventional electro-optic sampling (EOS) detection scheme, contributes significantly to the measured signal in 2D Ram
Externí odkaz:
http://arxiv.org/abs/2407.09243
AI-based diagnoses have demonstrated dermatologist-level performance in classifying skin cancer. However, such systems are prone to under-performing when tested on data from minority groups that lack sufficient representation in the training sets. Al
Externí odkaz:
http://arxiv.org/abs/2406.18375
Continual Test-Time Adaptation (CTTA) seeks to adapt a source pre-trained model to continually changing, unlabeled target domains. Existing TTA methods are typically designed for environments where domain changes occur sequentially and can struggle i
Externí odkaz:
http://arxiv.org/abs/2406.10737
Vision Transformers (ViTs) have demonstrated remarkable capabilities in learning representations, but their performance is compromised when applied to unseen domains. Previous methods either engage in prompt learning during the training phase or modi
Externí odkaz:
http://arxiv.org/abs/2407.09498
Gauging the performance of ML models on data from unseen domains at test-time is essential yet a challenging problem due to the lack of labels in this setting. Moreover, the performance of these models on in-distribution data is a poor indicator of t
Externí odkaz:
http://arxiv.org/abs/2405.01451
Autor:
Bell, Brian, Geyer, Michael, Glickenstein, David, Hamm, Keaton, Scheidegger, Carlos, Fernandez, Amanda, Moore, Juston
There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high-dimensionality of the data, high codimension in the ambient space of the data manifolds of interest, and that the st
Externí odkaz:
http://arxiv.org/abs/2404.08069