Results 231 to 240 of about 306,275 (290)
Prediction of the Minimum Miscibility Pressure of the CO<sub>2</sub>‑Crude Oil System in the Ordos Basin. [PDF]
Zhu Q +6 more
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Kilohertz volumetric imaging of in vivo dynamics using squeezed light field microscopy. [PDF]
Wang Z +19 more
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Tips and Tricks for Installation of the SLIM Nail in Osteogenesis Imperfecta with Narrow Medullary Canals: A Surgical Guide with Case Insights. [PDF]
Mounsef PJ, Legler J, Hamdy R.
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Massive calcified solid pseudopapillary neoplasm of the pancreatic head. [PDF]
Limaiem F, Hajri M.
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Difficult bile-duct stones in surgical anatomy managed with entero-endoscopic retrograde cholangiopancreatography and a new slim cholangioscope. [PDF]
Spadaccini M +6 more
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Slimming ResNet by Slimming Shortcut
2020 25th International Conference on Pattern Recognition (ICPR), 2021Conventional network pruning methods on convolutional neural networks (CNNs) reduce the number of input or output channels of convolution layers. With these approaches, the channels in the plain network can be pruned without any restrictions. However, in the case of the ResNet based networks which have shortcuts (skip connections), the channel slimming
Donggyu Joo, Doyeon Kim, Junmo Kim
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IEEE Transactions on Medical Imaging, 1993
SLIM (spectroscopic localization by imaging), a tool for in vivo magnetic resonance spectroscopy that provides compartmental spectra from relatively few phase-encoded data, is limited by the inhomogeneities within each compartment. It is shown that the SLIM-derived spectra approach the average compartmental spectra as the number of phase encoded data ...
X, Hu, Z, Wu
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SLIM (spectroscopic localization by imaging), a tool for in vivo magnetic resonance spectroscopy that provides compartmental spectra from relatively few phase-encoded data, is limited by the inhomogeneities within each compartment. It is shown that the SLIM-derived spectra approach the average compartmental spectra as the number of phase encoded data ...
X, Hu, Z, Wu
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Proceedings of the Second Asia-Pacific Workshop on Systems, 2011
In the era of cloud computing, mobile applications often need to leverage a new storage hierarchy that includes not only the traditional main memory and secondary storage on devices, but also storage and computation capabilities from the cloud. In this paper we propose a new data structure library called SLIM. SLIM provides familiar STL-like interfaces
Jinghao Shi +4 more
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In the era of cloud computing, mobile applications often need to leverage a new storage hierarchy that includes not only the traditional main memory and secondary storage on devices, but also storage and computation capabilities from the cloud. In this paper we propose a new data structure library called SLIM. SLIM provides familiar STL-like interfaces
Jinghao Shi +4 more
openaire +1 more source

