In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[EX Q]: An octave convolutional layer BIBREF0 factorizes the output feature maps of a convolutional layer into two groups. The resolution of the low-frequency feature maps is reduced by an octave height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves dividing by $2^t$, where $t=1,2,3$ and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer, and an example with three groups and reductions of one and two octaves is depicted in Fig. FIGREF1.
Question: How is octave convolution concept extended to multiple resolutions and octaves?
[EX A]: The resolution of the low-frequency feature maps is reduced by an octave height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves dividing by $2^t$, where $t=1,2,3$ and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer,

[EX Q]: We propose a novel problem of relationship recommendation (RSR). Different from the reciprocal recommendation problem on DSNs, our RSR task operates on regular social networks (RSN), estimating long-term and serious relationship compatibility based on social posts such as tweets.
Question: Is this a task other people have worked on?
[EX A]: No

[EX Q]: The Y-axis in the figure is the success rate of the agent (measured in terms of number of dialogs that resulted in launching a skill divided by total number of dialogs), and the X-axis is the number of learning steps.
Question: How did they measure effectiveness?
[EX A]:
number of dialogs that resulted in launching a skill divided by total number of dialogs