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pro vyhledávání: '"Green, James R"'
High-resolution images offer more information about scenes that can improve model accuracy. However, the dominant model architecture in computer vision, the vision transformer (ViT), cannot effectively leverage larger images without finetuning -- ViT
Externí odkaz:
http://arxiv.org/abs/2405.13985
A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled, spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable. We present CROMA: a framework that combines contrastive and recons
Externí odkaz:
http://arxiv.org/abs/2311.00566
Across many domains, real-world problems can be represented as a network. Nodes represent domain-specific elements and edges capture the relationship between elements. Leveraging high-performance computing and optimized link prediction algorithms, it
Externí odkaz:
http://arxiv.org/abs/2210.11616
Infant pose monitoring during sleep has multiple applications in both healthcare and home settings. In a healthcare setting, pose detection can be used for region of interest detection and movement detection for noncontact based monitoring systems. I
Externí odkaz:
http://arxiv.org/abs/2210.00662
Although the remote sensing (RS) community has begun to pretrain transformers (intended to be fine-tuned on RS tasks), it is unclear how these models perform under distribution shifts. Here, we pretrain a new RS transformer--called SatViT-V2--on 1.3
Externí odkaz:
http://arxiv.org/abs/2209.14969
Autor:
Charih, François, Green, James R.
A number of private and public insurers compensate workers whose hearing loss can be directly attributed to excessive exposure to noise in the workplace. The claim assessment process is typically lengthy and requires significant effort from human adj
Externí odkaz:
http://arxiv.org/abs/2208.14621
Akademický článek
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Autor:
Nizami, Shermeen, Bekele, Amente, Hozayen, Mohamed, Greenwood, Kim, Harrold, JoAnn, Green, James R.
Publikováno v:
Proc. of IEEE Int. Symp. Med. Meas. Appl. (MeMeA), Rochester, MN, USA, 2017
Pressure-sensitive mats (PSM) have proved to be useful in the estimation of respiratory rates (RR) in adult patients. However, PSM technology has not been extensively applied to derive physiologic parameters in infant and neonatal patients. This rese
Externí odkaz:
http://arxiv.org/abs/1805.00083
Publikováno v:
IEEE Reviews in Biomedical Engineering, 2013
Artifact Detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in Critical Care Units (CCU) by assessing quality of data prior to Clinical Event Detection (CED) and Parameter Derivation (PD). This methodological revi
Externí odkaz:
http://arxiv.org/abs/1805.00086