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Multiple-Instance Learning (MIL) refers to the problem wherein each object is a bag consisting of multiple instances and only the bags' labels are provided.
However, there are general agreements that insightful problem solving can be characterized by four salient features as follows: Mental impasse: There is a high probability that the problem solver experiences an impasse in the process of solving the problem, wherein the solver is mentally stuck on an unsuitable construct of the problem and fails to progress further [11].
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We will focus on the consensus problem, wherein each peer in a set of peers {P 1,…,P n } starts with an initial value v i ∈M for an arbitrary fixed set M. At some point during the execution of the consensus protocol, each peer irrevocably decides on some output value (hat {v}_{i} in M).
The DCF of IEEE802.11 suffers from the rate anomaly problem, wherein the stations transmitting at a slower rate get more airtime as compared with stations transmitting at higher rates [22].
We define the problem of maximal assortativity matching (MAM) as a variant of the maximal matching problem wherein we want to maximize the similarity between the end vertices (with respect to any particular measure for node weight) constituting the matching.
For evaluating the accuracy and the efficiency of the proposed approach, a numerical viscoelastic problem wherein the material properties are assumed to be exponential functions of the Cartesian coordinate x, is considered.
This is similar to the subcarrier allocation problem, wherein no adjacent receiver transmitter pairs should be allocated the same subcarrier.
Such a mathematical description of porous media on the macroscale leads to a volume-coupled multi-field problem, wherein the interface between the two phases is not resolved explicitly.
To this purpose the problem is formulated as an unconstrained least-square distance problem wherein the optimisation variables are all the parameters tuning the shape of the NURBS hypersurface.
In Section 2, we give the system model and formulate our problem, wherein the mean intercell interference tables of OFDM and FBMC are introduced.
A more accurate structure of the flux J is found by solving an equivalent electrostatic problem, wherein the concentration ϕ sat, respectively the saturated pressure P sat is an electrostatic potential and the meniscus, with its fixed potential, is a conductor with a neck shape.
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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