Coronavirus map: Tracking cases in the U.S. and around the world - Washington Post

[AzertFurce]

Coronavirus map: Tracking cases in the U.S. and around the world - Washington Post Biden vows to double aid for vulnerable nations dealing with climate change

It is composed of 300 water Cherenkov detectors (WCD). Each of them is a cylinder of 7.3 m in diameter and 4.5 m in height. POSTSUPERSCRIPT. They are equipped with 4 upward facing photomultiplier tubes (PMT), one 10” Hamamatsu R7081-HQE at the centre of the tank and three 8” R5912 halfway to the edges. 95%. Those two characteristics compensate the smaller instantaneous sensitivity compared to the imaging atmospheric Cherenkov technique, especially at the highest energies for which fluxes tend to be low. Around 10 TeV, the footprint on the ground produced by a gamma-ray shower is comparable to the size of the instrument. Therefore, most of the events triggering the instrument will fall outside the main array which makes the event reconstruction less accurate because of degeneracy between the core position and the energy of the shower. In order to improve the reconstruction of those events thus improving the instrument sensitivity, a sparse outrigger array of small tanks has been added around the main array.
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Moreover, proposed approaches for video semantic segmentation are often complex and computationally demanding. Further, traditional approaches treat semantic segmentation as a purely local problem, often training models using loss functions that penalize errors only at a pixel level. This approach has proven effective when working with large and well labeled datasets. However, intrinsic regularities in the data, such as the spatial relationship between anatomical structures in surgical videos, could be leveraged in the output space to boost performance even on smaller datasets. To address these challenges, we propose a framework for semi-supervised video semantic segmentation, Temporally Constrained Neural Networks (TCNN). To validate our approach, we benchmark our method on two challenging surgical video datasets for both tool and anatomy segmentation. We believe this is the first work explicitly designed on the video semantic segmentation of anatomical classes in surgical videos. We generate a new dataset for the segmentation of fine-grained anatomical structures in surgical videos. We demonstrate that autoencoder networks can be used to regularize models on highly variable and dynamic surgical videos.


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Coronavirus map: Tracking cases in the U.S. and around the world - Washington Post
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Coronavirus map: Tracking cases in the U.S. and around the world - Washington Post
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Coronavirus map: Tracking cases in the U.S. and around the world - Washington Post
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Coronavirus map: Tracking cases in the U.S. and around the world - Washington Post
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[GregoryZer]

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[GregoryZer]

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[GregoryZer]

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