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

書目名稱Hip Fractures影響因子(影響力)




書目名稱Hip Fractures影響因子(影響力)學(xué)科排名




書目名稱Hip Fractures網(wǎng)絡(luò)公開度




書目名稱Hip Fractures網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Hip Fractures被引頻次




書目名稱Hip Fractures被引頻次學(xué)科排名




書目名稱Hip Fractures年度引用




書目名稱Hip Fractures年度引用學(xué)科排名




書目名稱Hip Fractures讀者反饋




書目名稱Hip Fractures讀者反饋學(xué)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

厭煩

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