Zobrazeno 1 - 10
of 2 437
pro vyhledávání: '"Fox, P. C."'
Deep learning has proven very promising for interpreting MRI in brain tumor diagnosis. However, deep learning models suffer from a scarcity of brain MRI datasets for effective training. Self-supervised learning (SSL) models provide data-efficient and
Externí odkaz:
http://arxiv.org/abs/2411.12874
Autor:
Toledo-Marin, J. Quetzalcoatl, Gonzalez, Sebastian, Jia, Hao, Lu, Ian, Sogutlu, Deniz, Abhishek, Abhishek, Gay, Colin, Paquet, Eric, Melko, Roger, Fox, Geoffrey C., Swiatlowski, Maximilian, Fedorko, Wojciech
Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Model and searches for new phenomena. Simulations of collision events at
Externí odkaz:
http://arxiv.org/abs/2410.22870
The traffic in cislunar space is expected to increase over the coming years, leading to a higher likelihood of conjunction events among active satellites, orbital debris, and non-cooperative satellites. This increase necessitates enhanced space domai
Externí odkaz:
http://arxiv.org/abs/2410.06425
The significant expansion of the orbital debris population poses a serious threat to the safety and sustainability of space operations. This paper investigates orbital debris remediation through a network of collaborative space-based lasers, leveragi
Externí odkaz:
http://arxiv.org/abs/2409.03146
Autor:
Chen, Mengkun, Liu, Yen-Tung, Khan, Fadeel Sher, Fox, Matthew C., Reichenberg, Jason S., Lopes, Fabiana C. P. S., Sebastian, Katherine R., Markey, Mia K., Tunnell, James W.
Publikováno v:
Computerized Medical Imaging and Graphics, volume = {118}, pages = {102468}, year = {2024}, issn = {0895-6111}
Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, virtual staining offers a
Externí odkaz:
http://arxiv.org/abs/2405.13278
Autor:
Chennamsetti, Varshitha, von Laszewski, Gregor, Gu, Ruochen, Mehnaz, Laiba, Papay, Juri, Jackson, Samuel, Thiyagalingam, Jeyan, Samsonau, Sergey V., Fox, Geoffrey C.
In this paper, we report on work performed for the MLCommons Science Working Group on the cloud masking benchmark. MLCommons is a consortium that develops and maintains several scientific benchmarks that aim to benefit developments in AI. The benchma
Externí odkaz:
http://arxiv.org/abs/2401.08636
Autor:
von Laszewski, Gregor, Chang, Wo, Reinsch, Russell, Kotevska, Olivera, Karimi, Ali, Sattar, Abdul Rahman, Mazzaferro, Garry, Fox, Geoffrey C.
Over the last several years, the computation landscape for conducting data analytics has completely changed. While in the past, a lot of the activities have been undertaken in isolation by companies, and research institutions, today's infrastructure
Externí odkaz:
http://arxiv.org/abs/2310.17013
In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a minimalistic approach while at the same time allowing to access c
Externí odkaz:
http://arxiv.org/abs/2210.16941
Autor:
Feng, Bo, Fox, Geoffrey C.
Publikováno v:
IEEE eScience 2021
Geoscience and seismology have utilized the most advanced technologies and equipment to monitor seismic events globally from the past few decades. With the enormous amount of data, modern GPU-powered deep learning presents a promising approach to ana
Externí odkaz:
http://arxiv.org/abs/2012.14336
Publikováno v:
The 2021 Conference on Empirical Methods in Natural Language Processing
Billions of text analysis requests containing private emails, personal text messages, and sensitive online reviews, are processed by recurrent neural networks (RNNs) deployed on public clouds every day. Although prior secure networks combine homomorp
Externí odkaz:
http://arxiv.org/abs/2010.11796