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By using causal prediction, the predicted value can be rounded to integer number for enhancing coding efficiency since the prediction context of a pixel has been known, such as the median edge direction (MED) predictor in JPEG-LS standard [30].
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The experiments were done by using causal reverberation model only.
The experiments were done by using causal reverberation model (using left context only) and non-causal reverberation model (using left and right context) on known environments (matched conditions).
The experiments were done by using causal reverberation model and skip1 frame selection only.
Nonetheless, the results indicate that paraphrases of indicative causal conditionals convey the multiple possibilities consistent with the causal conditional by using causal or temporal connectives.
People paraphrase indicative causal conditionals without using 'if' by using causal or temporal connectives (e.g., because, when); they paraphrase counterfactual causal conditionals by using subjunctive constructions (e.g., had A happened, B would have happened).
Experiments 2a and 2b showed that people paraphrased causal conditionals by using causal or temporal connectives (because, when), whereas they paraphrased causal counterfactuals by using subjunctive constructions (had…would have).
In contrast, they paraphrased indicative causal conditionals primarily by using causal connectives such as 'because' and temporal connectives such as 'when'.
This would be confirm or not using the FROG-ICU data notably by using causal inference approaches.
This is especially useful in models using causal covariates applied to the prediction of future forest status.
Depending on whether a study uses causal or prediction models, a proper distinction between concepts of multimorbidity is important.
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