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Mathematically this can be stated as, Given: v j,v k ∈N v i ), where: ℓ[v k ]≼ℓ[v j ]≼ℓ[v i ]. then: { v j, v k } ⃗ ⊈ V ⃗. if: v k ∉N v j ).

Now, the corresponding result for Theorem 3 can be stated as: Given an equivalence class A with a as the rightmost leaf of A c, 1. E T yz is pruned by A if (i) for every T yb ∈ E T y, E T yb.

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The problem of texture synthesis can be stated as follows: Given a texture sample, synthesize a new texture that, when perceived by a human observer, appears to be generated by the same underlying stochastic process.

Our problem then may be stated as follows: given y - ( t ), our aim is to preserve x1 t) while minimizing the contribution of v - ( t ).

The traditional person re-identification problem can be stated as follows: Given n persons crossing camera 1, their images are captured to generate an image set, named gallery images.

The tampering detection problem can be stated as follows: given an image, detect whether it has been tampered through crop-and-paste and if so, locate the region where the crop-and-paste occurred.

The problem of motion estimation can be stated as follows: "Given an image sequence, compute a representation of the motion field that best aligns pixels in one frame of the sequence with those in the next" [9].

Following this notation, the EEG inverse problem can be stated as follows: Given a set of EEG signals (Y) and a forward model (A), estimate the current sources within the brain (S) that produce these signals.

The Euclidean distance problem can now be stated as follows: Given a distance matrix F ∈ R n × n, find the Euclidean distance matrix D ∈ R n × n that minimizes ∥ F − D ∥ F, (2.2).

Summarizing, our synthetic problems can be stated as follows: Given matrices Y ∈ R 128 × 161 and A ∈ R 128 × N, recover the synthetic BES matrix S ∈ R N × 161.

The texture synthesis problem can be stated as follows: given a finite sample texture, a system must automatically create an outcome with similar visual attributes of the input and a predefined size.

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