The phrase "a representation from" is correct and usable in written English.
It can be used when referring to a depiction or illustration that originates from a specific source or individual.
Example: "The report included a representation from the local artist, showcasing the community's culture."
Alternatives: "a depiction from" or "an illustration from".
"A lot of purchasers' attorneys like to include a representation from the seller that the roof is free of leaks, that the basement is dry and free of seepage and that the septic system and well do not serve more than the premises being purchased".
A clue to the puzzle lay in the discovery of an annotated version of Masalik al-abşar fi mamalik al-amşar by Ibn Fadlallah al-Umari, a representation from 1340 of a lost map of the world prepared by the geographers of the Abbasid caliph al-Ma'mun in the eighth century.
It also drew attention to the problem of distinguishing the content determining causes of a representation from adventitious non-content determining causes.
"I think it's important that we have a representation from the four nations and I think that gives a good balance to the team," he said.
The WWW2 conference (Chicago, October 1994) had a focus on the development of the HTML markup language and was noteworthy for the attendance of non-scientists and many commercial organisations, as well as a representation from chemists [29].
Suppose that a pixel or sample x in hyperspectral image is (phantom {dot {i}!}boldsymbol {x} = (x_{1}, x_{2},ldots, x_{n_{0}})^{T}), n 0 is the number of the spectral bands, and (boldsymbol {r}^{(l)} = left ({r}^{(l)}_{1}, {r}^{(l)}_{2},ldots, {r}^{(l)}_{n_{l}}right)^{T}) is the output of hidden layer l or a representation from hidden layer l, and n l is the number of the units in layer l.
This paper proposes a feature transformation scheme to learn a histogram representation from an unordered feature set.
The memory system can be sampled using cues, consisting of partial input patterns; for instance, part of a context representation from a previously experienced episode.
This work proposes a methodology to learn a speech representation from data by optimising a filter bank, in order to improve results in the classification of stressed speech.
The Bio-LDA model provides an unsupervised method for extracting an interpretable representation from a collection of documents.
Similarly, in quadrant B, we also observe an even contribution from a wide range, with a stronger representation from the bacteroides genus.
