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Our fusion method collects the activity labels over frames and cameras, and then fuses activity judgments as the sequence label.
The real-valued observations relate to the sequence label only through the latent copy number's variables, making the sequence label conditionally independent of the observed measurements given the copy numbers.
Furthermore, we assume the sequence label to be directly affected by only a small subset of positions in the copy number profile.
The features array contains information for each feature to be displayed including, for instance, identifier, start and end positions in the sequence, label, and color among others.
A community matrix was then created from the mapping file for each locus by retrieving the sample information in each sequence label and cross referencing with OTU identity of the read.
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The merits of our approach are demonstrated on two sequence labeling tasks.
Named entity recognition (NER) is mostly formalized as a sequence labeling problem in which segments of named entities are represented by label sequences.
Experiments on various sequence labeling tasks demonstrate that the proposed pre-training method consistently yields significant improvement in performance.
Sequence labeling is the widely used method for CCG supertagging task where a supertag (lexical category) is assigned to each word in an input sentence.
Opinion target recognition can be deemed a problem of token-level sequence labelling, and each word in the sentence is assigned to a label with the standard BIO tagging scheme.
Sequence labeling: There is a somewhat arbitrary line between the preceding task and sequence labeling.
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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