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20,991 to 21,000 of 21,092 Results

Sample 1 of human femoral heads: micro-XRCT data sets Mar 30, 2021 - micro X-ray computed tomography data sets of human femoral heads Ruf, Matthias; Steeb, Holger; Gebert, Johannes; Schneider, Ralf; Helwig, Peter, 2021, "Sample 1 of human femoral heads: micro-XRCT data sets", https://doi.org/10.18419/DARUS-1177, DaRUS, V1 Data contains micro X-Ray Computed Tomography (micro-XRCT) data sets of an explanted femoral head. This specimen was donated by a male patient at the age of 74 and a bodyweight of 128 kg who received a hip endoprosthesis. Additional information was not given. The explantation took place in cooperation with Prof. Dr. Peter Helwig at the Clinic Heide...
reconstructed_20201125_01.rar Mar 30, 2021 - Sample 1 of human femoral heads: micro-XRCT data sets RAR Archive - 22.1 GB - MD5: 1f0d0e687f4998b721a22089445b7497 Reconstructed micro-XRCT data set in 16 bit *.tif file format. 2940x2940x2141 voxels with the uniform voxel size of 15.0 µm. Overview scan.
reconstructed_20201126_01.rar Mar 30, 2021 - Sample 1 of human femoral heads: micro-XRCT data sets RAR Archive - 24.0 GB - MD5: 79840054a7070d15925ca348fe6c2773 Reconstructed micro-XRCT data set in 16 bit *.tif file format. 2940x2940x2141 voxels with the uniform voxel size of 5.0 µm. Lower part scan.
reconstructed_20201127_01.rar Mar 30, 2021 - Sample 1 of human femoral heads: micro-XRCT data sets RAR Archive - 23.8 GB - MD5: 526700772056245bbcbde7ebc70ddbd9 Reconstructed micro-XRCT data set in 16 bit *.tif file format. 2940x2940x2141 voxels with the uniform voxel size of 5.0 µm. Upper part scan.
Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression Mar 23, 2021 - PN 6-3 Holzmüller, David, 2021, "Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression", https://doi.org/10.18419/DARUS-1771, DaRUS, V1 This dataset contains code used to generate the figures in the paper On the Universality of the Double Descent Peak in Ridgeless Regression, David Holzmüller, International Conference on Learning Representations 2021. The code is also provided on GitHub. Here, we additionally provide the data that is generated by the code and that is required to ge...
computation.py Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression Python Source Code - 16.2 KB - MD5: 727ea9050972d03f12c6aa5b32b7e37c
data.tar.gz Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression Gzip Archive - 5.6 GB - MD5: 220970917c2371206a808c02bbb9f359 Data Data generated by running the code
main.py Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression Python Source Code - 2.7 KB - MD5: d298130fc29f0a84b33b58ad0d100a06
plotting.py Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression Python Source Code - 15.3 KB - MD5: 9017e22f361d3ee27b7fa52a241a2834
README.md Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression Markdown Text - 1.6 KB - MD5: d2572a82e8003c56d0307012ac4069d8

Sample 1 of human femoral heads: micro-XRCT data sets

Mar 30, 2021 - micro X-ray computed tomography data sets of human femoral heads

Ruf, Matthias; Steeb, Holger; Gebert, Johannes; Schneider, Ralf; Helwig, Peter, 2021, "Sample 1 of human femoral heads: micro-XRCT data sets", https://doi.org/10.18419/DARUS-1177, DaRUS, V1

Data contains micro X-Ray Computed Tomography (micro-XRCT) data sets of an explanted femoral head. This specimen was donated by a male patient at the age of 74 and a bodyweight of 128 kg who received a hip endoprosthesis. Additional information was not given. The explantation took place in cooperation with Prof. Dr. Peter Helwig at the Clinic Heide...

reconstructed_20201125_01.rar

Mar 30, 2021 - Sample 1 of human femoral heads: micro-XRCT data sets

RAR Archive - 22.1 GB -

Reconstructed micro-XRCT data set in 16 bit *.tif file format. 2940x2940x2141 voxels with the uniform voxel size of 15.0 µm. Overview scan.

reconstructed_20201126_01.rar

Mar 30, 2021 - Sample 1 of human femoral heads: micro-XRCT data sets

RAR Archive - 24.0 GB -

Reconstructed micro-XRCT data set in 16 bit *.tif file format. 2940x2940x2141 voxels with the uniform voxel size of 5.0 µm. Lower part scan.

reconstructed_20201127_01.rar

Mar 30, 2021 - Sample 1 of human femoral heads: micro-XRCT data sets

RAR Archive - 23.8 GB -

Reconstructed micro-XRCT data set in 16 bit *.tif file format. 2940x2940x2141 voxels with the uniform voxel size of 5.0 µm. Upper part scan.

Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression

Mar 23, 2021 - PN 6-3

Holzmüller, David, 2021, "Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression", https://doi.org/10.18419/DARUS-1771, DaRUS, V1

This dataset contains code used to generate the figures in the paper On the Universality of the Double Descent Peak in Ridgeless Regression, David Holzmüller, International Conference on Learning Representations 2021. The code is also provided on GitHub. Here, we additionally provide the data that is generated by the code and that is required to ge...

computation.py

Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression

Python Source Code - 16.2 KB -

data.tar.gz

Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression

Gzip Archive - 5.6 GB -

Data

Data generated by running the code

main.py

Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression

Python Source Code - 2.7 KB -

plotting.py

Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression

Python Source Code - 15.3 KB -

README.md

Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression

Markdown Text - 1.6 KB -

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