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We randomly selected 80%% of the extracted frames for training and the remaining for testing.
The extracted frames are ordered chronologically, which is referred to as the skimmed video in this paper.
From each of the clusters obtained by the above process, we extract several frames that are close to the center of the cluster where the number of extracted frames is defined to be proportional to the cluster size.
Each inserted OPE molecule has been numerically labeled to correspond to the extracted frames in 3, which display the most dynamic 25 frames for each inserted molecule.
We further extracted frames covering two nonoverlapping time intervals (one interval at the beginning and one toward the end of the sequence, both spanning at least two heartbeat periods) from both the original sequence and the left and right sequences.
The first step was to build Human-mouse-rat ortholog triplets, from which frame +1 and -1 ORFs were extracted (frames +1 and -1 are also sometimes known as frames 2 and 3, respectively).
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Marker flags show the emplacement of Acesta clams and the grey box delineates the outline of the extracted frame shown in (b).
The WB audio feature F X (m) and the HF spectral envelope F Y (m) are separately extracted frame by frame from the aligned WB and SWB audio signals, which are available for the training procedure, so as to form the training sample data [F X (m) T, F Y (m) T ] T, where m = 0,1,…, N train − 1 denotes the index of audio frame.
In short, the main idea in the frame reduction approach is to partition the extracted frame features into distinct clusters (based on their similarity/distance between each other) and to select only one or few key-frames from each cluster to represent its corresponding sound.
We will be interested in how to automatically construct an inverse story-board (a visual summary) from video, and how to use the audio track to extract frames of interest.
A method for extracting frames from videos to help professors and students find their way through the videos had been devised, a nifty trick invented by one of the two undergraduates, Kelvin Do.
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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