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L is a subclass of NL, which is the class of languages decidable in logarithmic space on a nondeterministic Turing machine.A problem in NL may be transformed into a problem of reachability in a directed graph representing states and state transitions of the nondeterministic machine, and the logarithmic space bound implies that this graph has a polynomial number of vertices and edges, from ...
However, log-space reductions are probably weaker than polynomial-time reductions; while any non-empty, non-full language in P is polynomial-time reducible to any other non-empty, non-full language in P, a log-space reduction from an NL-complete language to a language in L, both of which would be languages in P, would imply the unlikely L = NL.
In computational complexity theory, a log-space computable function is a function : that requires only () memory to be computed (this restriction does not apply to the size of the output). The computation is generally done by means of a log-space transducer .
A language is said to be log-space reducible to a language if there exists a log-space computable function that will convert an input from problem into an input to problem in such a way that (). This seems like a rather convoluted idea, but it has two useful properties that are desirable for a reduction:
In computational complexity theory, NL (Nondeterministic Logarithmic-space) is the complexity class containing decision problems that can be solved by a nondeterministic Turing machine using a logarithmic amount of memory space. NL is a generalization of L, the class for logspace problems on a deterministic Turing machine.
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Small combines the works and findings of quite a few scientists to come up with five reasons why spaced repetition works: it helps show the relationship of routine memories, it shows the benefits of learning things with an expansion of time, it helps the patient with Alzheimer's dementia keep their brain active, it has a high success level with ...
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]