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Jesus and the Cross - Biblical Archaeology Society Throughout the world, images of the cross adorn the walls and steeples of churches For some Christians, the cross is part of their daily attire worn around their necks Sometimes the cross even adorns the body of a Christian in permanent ink In Egypt, among other countries, for example, Christians wear a tattoo of the cross on their wrists And for some Christians, each year during the
How Was Jesus Crucified? - Biblical Archaeology Society Gospel accounts of Jesus’s execution do not specify how exactly Jesus was secured to the cross Yet in Christian tradition, Jesus had his palms and feet pierced with nails Even though Roman execution methods did include crucifixion with nails, some scholars believe this method only developed after Jesus’s lifetime
Cross-attention mask in Transformers - Data Science Stack Exchange Cross-attention mask: Similarly to the previous two, it should mask input that the model "shouldn't have access to" So for a translation scenario, it would typically have access to the entire input and the output generated so far So, it should be a combination of the causal and padding mask
The Staurogram - Biblical Archaeology Society The staurogram combines the Greek letters tau-rho to stand in for parts of the Greek words for “cross” (stauros) and “crucify” (stauroō) in Bodmer papyrus P75 Staurograms serve as the earliest images of Jesus on the cross, predating other Christian crucifixion imagery by 200 years Photo: Foundation Martin Bodmer
Ancient Crucifixion Images - Biblical Archaeology Society This second-century graffito of a Roman crucifixion from Puteoli, Italy, is one of a few ancient crucifixion images that offer a first-hand glimpse of Roman crucifixion methods and what Jesus’ crucifixion may have looked like to a bystander
When to use cross-validation? - Data Science Stack Exchange I have noticed that when I use 10-fold cross-validation in the training dataset I get low evaluation metrics compared to training the model on the entire dataset without cross-validation, this can make sense if I get better performance for the model when I test it on the testing dataset, however, it keeps the same performance