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A quine's output is exactly the same as its source code. A quine is a computer program that takes no input and produces a copy of its own source code as its only output. The standard terms for these programs in the computability theory and computer science literature are "self-replicating programs", "self-reproducing programs", and "self-copying programs".
State-based CRDTs (also called convergent replicated data types, or CvRDTs) are defined by two types, a type for local states and a type for actions on the state, together with three functions: A function to produce an initial state, a merge function of states, and a function to apply an action to update a state.
"Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.
The last of the non-avian dinosaurs died 66 million years ago in the course of the Cretaceous–Paleogene extinction event, whereas the earliest members of the genus Homo (humans) evolved between 2.3 and 2.4 million years ago. This places a 63-million-year expanse of time between the last non-avian dinosaurs and the earliest humans.
For every 3 non-theme words you find, you earn a hint. Hints show the letters of a theme word. If there is already an active hint on the board, a hint will show that word’s letter order.
Image credits: Bored Panda #2 Dave Grohl Fathered A Baby Girl Outside Of His Marriage. Lead singer of the Foo Fighters Dave Grohl isn’t trying to hide his truth. Back in September, the musician ...
Jahmyr Gibbs stirred up a controversy by posting a photo that contained info on the Lions' protection calls, but Dan Campbell isn't sweating it.
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.