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For the elevation angle (θ k ), it is estimated from the eigenvectors of R BA in a similar way as discussed in the EDBA estimator explained in Section 3. We will not repeat the discussion and derivation again in this section but rather we will give the final procedure on how to estimate the elevation angle θ k.
Understandably, the authors did not want to repeat the discussion of the algorithm given previously by Balakrishnan et al. and also briefly in their PNAS paper (Kamisetty et al., 2013).
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Relevance to the main themes of the symposium: The side events should not repeat the discussions by the thematic sessions but complement them and/or analyze various topics from different perspectives.
By repeating the discussion above, one can conclude that { y n } is Cauchy.
Then repeating the discussion in Lemma 3.4, we know that p ∈ F ( S n ).
end{aligned} Repeating the discussion in [23] (see p.1072 of it), we find that (I_{1}), (I_{2}), (I_{3}) tend to zero as (t rightarrow t') independently of (xin B).
Repeating the discussion above, we can see eventually that there exist some (T^{{prime}}geq T) and (lin { 1,3,ldots,2n-1 } ) such that for (tgeq T^{{prime}}), left { textstylebegin{array}l} x^{ ( i ) } ( t ) >0,quad i=0,1,ldots,l; ( -1 ) ^{ ( i-1 ) }x^{ ( i ) } ( t ) >0, quad i=l+1,ldots,2n-1.
Again, via alternatively repeating the discussions, we get (I t geq m_{3}) for all (t>t_{3}).
We will not try to repeat the overall discussion here, but just refer to some good references ([ 5- 11]).
"So there is no torture," the king repeats, closing the discussion firmly.
I agree: all too often, information is repeated or the discussion goes off-topic.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com