Titlebook: Hip Fractures; A Practical Guide to Kenneth J. Koval,Joseph D. Zuckerman Book 2000 Springer Science+Business Media New York 2000 anatomy.co

JOLT

书目名称Hip Fractures影响因子(影响力)




书目名称Hip Fractures影响因子(影响力)学科排名




书目名称Hip Fractures网络公开度




书目名称Hip Fractures网络公开度学科排名




书目名称Hip Fractures被引频次




书目名称Hip Fractures被引频次学科排名




书目名称Hip Fractures年度引用




书目名称Hip Fractures年度引用学科排名




书目名称Hip Fractures读者反馈




书目名称Hip Fractures读者反馈学科排名

obsolete

n models across different application domains. Extensive experiments were carried using different established methods: EAST, CRAFT, Tessaract and Ensembles applied to various publicly available datasets. The generalisation performance of the models was evaluated and compared using precision, recall

OPINE

Kenneth J. Koval,Joseph D. Zuckermanificantly sparser modifications than other approaches, we achieve comparable or better performance on all metrics. Moreover, we demonstrate that our approach predominantly modifies salient time steps and features, leaving non-salient inputs untouched.

Type-1-Diabetes

元音

Kenneth J. Koval,Joseph D. Zuckermantatistical features, and the SR-Module is composed of our proposed DUSCMAnet, a lightweight SR network. After classifying, a majority of sub-images will pass through lighter networks, thus the computational cost can be significantly reduced. Experiments show that our DUSCMAnet is superior to the exi

Nonconformist

厌烦

Kenneth J. Koval,Joseph D. Zuckermanontent, . employs deep neural networks to learn their representations automatically. Moreover, we propose a strategy to generate portfolios by utilizing prediction results obtained by .. Extensive data analysis and experiments show that . can bring us high-yield portfolios.

gnarled

Root494

Desert

Kenneth J. Koval,Joseph D. Zuckermanin-specific and domain-invariant features is enlarged, which promotes RPN feature to contain more domain-invariant information. Furthermore, we propose dynamic weighted adversarial training to alleviate the unstable training caused by adversarial learning. We conduct extensive experiments on multipl